The background of the invention is described with reference to the prior art references identified in Appendix A, attached hereto. The task of detection and localization of defects in VLSI wafers and masks is an essential but exhausting procedure. As the complexity of integrated circuits is increasing rapidly, the need to automate the inspection of photomasks and wafers becomes a more important necessity for maintaining high throughput and yield in the fabrication processes. Human visual inspection and electrical testing are the most widely used methods for defect detection; however, this is a time consuming and difficult task for people to do reliably. On the other hand the usage of electrical test is inherently limited to off-line and overall functional verification of the chip structure, and can only be accomplished after the fabrication is completed; it cannot be applied to on-line and layer by layer inspection of the wafer during the fabrication process. In addition to the need for inspecting wafers, the inspection of the mask pattern is critical because any defect on the mask is transferred to the wafers.
Typical patterns found in wafers and masks can be put into three main classes 1!:
Constant areas PA1 Straight lines PA1 Repeating structures
The repeating structure class covers two different cases. The first includes repeated patterns within a single chip such as memory areas, shift registers, adders, and switch capacitors. The chips themselves considered as repeated patterns on a wafer can be included in the second case. As another potential example of repeating patterns, one can mention LCD displays and arrays of charge-coupled devices (CCDs) arising in imaging systems and cameras.
Most inspection techniques fall into one of the following general categories: methods for checking generic properties and design rules, and methods based on image-to-image comparison. In the first category, the image is tested against a set of design rules or local properties and violations are reported as defects. An example of this kind of techniques is the work of Ejiri et al 2! that uses an expansion-contraction method to locate the defects. In image to image comparison methods, the image taken from the wafer is compared either with an ideal image stored in a database, or with the image taken from another region of the same wafer that is supposedly identical to the image under the test. A fairly complete review of the related literature may be found in 3!.
Several optical inspection techniques have been developed for locating and classifying defects on masks and wafers. In spatial filtering methods 4, 5, 6!, the spectrum of the perfect image in the Fourier transform domain is filtered out from the image and an image that includes only defective patterns is obtained. Since it is difficult to filter out only the frequencies of the acceptable pattern, these systems usually require large signal to noise ratios 7!. Also it is usually required to precisely align the filter and the sample. In addition, for each pattern, a separate filter has to be prepared 8!.
Most commercial inspection systems compare the chip patterns with a pre-stored image in a database. This requires a large volume of data as a reference. A data conversion step is also needed to make the scaling of the stored data equal to that of the inspected image 7!. As the size of devices decrease, proper adjustment of the scales for doing the required comparisons becomes more difficult to achieve. As an alternative to this approach that avoids the need for a large database, images of two adjacent dies can be compared; however, the detection is limited by step-and-repeat errors and also the errors in synchronizing the location of the two scanner beams over the die.
As was mentioned earlier, an important category of defect inspection applications is the inspection of repeated patterns on masks and wafers. This is a field of application for image-to-image comparison methods in which the repeatedness of the patterns is used. However, most existing systems that perform image to image comparison face the following difficulties. In order to compare images with each other, or with a reference database, accurate registration is necessary. This includes both problems of alignment and scaling and introduces a tradeoff between the minimum detectable defect size and the expense and throughput of the systems that compare the image with another image or a database image. Moreover, if an image to database comparison method is chosen, there is also the need to simulate the imaging and development processes in order to produce the database.
A self-reference technique that avoids the mentioned difficulties was developed by Dom et al 9!, in which the comparison is made using the repeated cells in the image. In this method prior knowledge about the period of repetition is assumed and scaling of the image is adjusted accordingly; then each pixel is compared with two corresponding pixels in left and right neighboring patterns.